Icd10 Ru Subgroup D
Multi-label classifier over 3-character ICD-10 subgroups inside chapter D. This specialist was distilled from local BERT teacher models into alexyalunin/RuBioBERT. Teacher weights are not uploaded to Hugging Face.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 680 MB
- Modelo base
- alexyalunin/RuBioBERT
- Versão
- vRevision 8932367
- Atualizado
- 9 de out. de 2026
- Publicador
- @Dmitry43243242
- Downloads
- 5
- Fonte
- Hugging Face
Descrição da fonte
ICD-10 subgroup classifier - group D (distilled specialist)
Multi-label classifier over 3-character ICD-10 subgroups inside chapter D.
This specialist was distilled from local BERT teacher models into alexyalunin/RuBioBERT. Teacher weights are not uploaded to Hugging Face.
Intended use / Назначение
- EN: Decision-support signal for suggesting candidate ICD-10 subgroups from Russian clinical notes. Not a substitute for clinician judgment; not validated for autonomous diagnosis.
- RU: Вспомогательный сигнал для предложения кандидатных 3-символьных кодов МКБ-10 по русскому клиническому тексту. Не заменяет врача и не предназначен для автономных клинических решений.
Training data / Обучающие данные
- Source CSV:
datasets/subgroups/group_D.csv - SHA-256:
10c1c6d836234bbd276eca3443a555ca9dfd77bab22f6ec5afcb6b938252fbc3 - Splits: train=528 · val=113 · test=112
- Labels: 57; rare/interface-only ids are listed in
label_map.json.
Training route
- Approach:
direct_hard_training_no_distillation - Base model:
alexyalunin/RuBioBERT - Direct validation hit@3:
0.9203539823008849 - No-distillation threshold:
0.9 - Teacher models (fallback KD only):
[] - Selected KD config (fallback only): temperature=
None, hard_loss_weight=None
Metrics (test split)
| metric | final specialist | teacher ensemble / fallback |
|---|---|---|
| macro_f1 | 0.6698 | |
| micro_f1 | 0.7299 | |
| weighted_f1 | 0.7358 | |
| subset_accuracy | 0.4732 | |
| hit@1 | 0.8571 | |
| hit@3 | 0.9286 | |
| recall@3 | 0.9286 | |
| mrr | 0.8996 |
Full per-label breakdown is available in metrics.json.
Inference
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
repo = "Dmitry43243242/icd10-ru-subgroup-d"
tok = AutoTokenizer.from_pretrained(repo)
mdl = AutoModelForSequenceClassification.from_pretrained(repo)
mdl.eval()
text = "жалобы пациента..."
inp = tok(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
probs = torch.sigmoid(mdl(**inp).logits)[0]
preds = [mdl.config.id2label[i] for i, p in enumerate(probs.tolist()) if p >= 0.5]
top5 = sorted(
[(mdl.config.id2label[i], p) for i, p in enumerate(probs.tolist())],
key=lambda x: -x[1],
)[:5]
print(preds, top5)
Fontes
1 fonte
Fontes
1 fonteVerificado 6 de ago.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- language: - ru license: apache-2.0 tags: - medical - icd-10 - multi-label-classification - russian - conditional-distillation base_model: alexyalunin/RuBioBERT pipeline_tag: text-classification --- # ICD-10 subgroup classifier - group D (distilled specialist) Multi-label classifier over 3-character ICD-10 subgroups inside chapter **D**. This specialist was distilled from local BERT teacher models into `alexyalunin/RuBioBERT`. Teacher weights are not uploaded to Hugging Face. ## Intended use / Назначение - **EN:** Decision-support signal for suggesting candidate ICD-10 subgroups from Russian clinical notes. **Not** a substitute for clinician judgment; not validated for autonomous diagnosis. - **RU:** Вспомогательный сигнал для предложения кандидатных 3-символьных кодов МКБ-10 по русскому клиническому тексту. **Не заменяет** врача и не предназначен для автономных клинических решений. ## Training data / Обучающие данные - Source CSV: `datasets/subgroups/group_D.csv` - SHA-256: `10c1c6d836234bbd276eca3443a555ca9dfd77bab22f6ec5afcb6b938252fbc3` - Splits: train=528 · val=113 · test=112 - Labels: 57; rare/interface-only ids are listed in `label_map.json`. ## Training route - Approach: `direct_hard_training_no_distillation` - Base model: `alexyalunin/RuBioBERT` - Direct validation hit@3: `0.9203539823008849` - No-distillation threshold: `0.9` - Teacher models (fallback KD only): `[]` - Selected KD config (fallback only): temperature=`None`, hard_loss_weight=`None` ## Metrics (test split) | metric | final specialist | teacher ensemble / fallback | |---|---:|---:| | macro_f1 | 0.6698 | | | micro_f1 | 0.7299 | | | weighted_f1 | 0.7358 | | | subset_accuracy | 0.4732 | | | hit@1 | 0.8571 | | | hit@3 | 0.9286 | | | recall@3 | 0.9286 | | | mrr | 0.8996 | | Full per-label breakdown is available in `metrics.json`. ## Inference ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch repo = "Dmitry43243242/icd10-ru-subgroup-d" tok = AutoTokenizer.from_pretrained(repo) mdl = AutoModelForSequenceClassification.from_pretrained(repo) mdl.eval() text = "жалобы пациента..." inp = tok(text, return_tensors="pt", truncation=True, max_length=512) with torch.no_grad(): probs = torch.sigmoid(mdl(**inp).logits)[0] preds = [mdl.config.id2label[i] for i, p in enumerate(probs.tolist()) if p >= 0.5] top5 = sorted( [(mdl.config.id2lab...
Source context: 5 downloads · 0 likes · Pipeline text-classification · Repo Dmitry43243242/icd10-ru-subgroup-d
Source context: 1 downloads · 0 likes · Pipeline text-classification · Repo Dmitry43243242/icd10-ru-subgroup-d
